diff --git a/lang/zh/gklearn/kernels/sylvester_equation.py b/lang/zh/gklearn/kernels/sylvester_equation.py index 3879b59..bf9cb2d 100644 --- a/lang/zh/gklearn/kernels/sylvester_equation.py +++ b/lang/zh/gklearn/kernels/sylvester_equation.py @@ -16,18 +16,18 @@ import numpy as np import networkx as nx from control import dlyap from gklearn.utils.parallel import parallel_gm, parallel_me -from gklearn.kernels import RandomWalk +from gklearn.kernels import RandomWalkMeta -class SylvesterEquation(RandomWalk): +class SylvesterEquation(RandomWalkMeta): def __init__(self, **kwargs): - RandomWalk.__init__(self, **kwargs) + super().__init__(**kwargs) def _compute_gm_series(self): - self._check_edge_weight(self._graphs) + self._check_edge_weight(self._graphs, self._verbose) self._check_graphs(self._graphs) if self._verbose >= 2: import warnings @@ -38,7 +38,7 @@ class SylvesterEquation(RandomWalk): # compute Gram matrix. gram_matrix = np.zeros((len(self._graphs), len(self._graphs))) - if self._q == None: + if self._q is None: # don't normalize adjacency matrices if q is a uniform vector. Note # A_wave_list actually contains the transposes of the adjacency matrices. if self._verbose >= 2: @@ -54,16 +54,16 @@ class SylvesterEquation(RandomWalk): # norm[norm == 0] = 1 # A_wave_list.append(A_tilde / norm) - if self._p == None: # p is uniform distribution as default. + if self._p is None: # p is uniform distribution as default. from itertools import combinations_with_replacement itr = combinations_with_replacement(range(0, len(self._graphs)), 2) if self._verbose >= 2: - iterator = tqdm(itr, desc='calculating kernels', file=sys.stdout) + iterator = tqdm(itr, desc='Computing kernels', file=sys.stdout) else: iterator = itr for i, j in iterator: - kernel = self.__kernel_do(A_wave_list[i], A_wave_list[j], lmda) + kernel = self._kernel_do(A_wave_list[i], A_wave_list[j], lmda) gram_matrix[i][j] = kernel gram_matrix[j][i] = kernel @@ -76,7 +76,7 @@ class SylvesterEquation(RandomWalk): def _compute_gm_imap_unordered(self): - self._check_edge_weight(self._graphs) + self._check_edge_weight(self._graphs, self._verbose) self._check_graphs(self._graphs) if self._verbose >= 2: import warnings @@ -85,7 +85,7 @@ class SylvesterEquation(RandomWalk): # compute Gram matrix. gram_matrix = np.zeros((len(self._graphs), len(self._graphs))) - if self._q == None: + if self._q is None: # don't normalize adjacency matrices if q is a uniform vector. Note # A_wave_list actually contains the transposes of the adjacency matrices. if self._verbose >= 2: @@ -94,7 +94,7 @@ class SylvesterEquation(RandomWalk): iterator = self._graphs A_wave_list = [nx.adjacency_matrix(G, self._edge_weight).todense().transpose() for G in iterator] # @todo: parallel? - if self._p == None: # p is uniform distribution as default. + if self._p is None: # p is uniform distribution as default. def init_worker(A_wave_list_toshare): global G_A_wave_list G_A_wave_list = A_wave_list_toshare @@ -113,7 +113,7 @@ class SylvesterEquation(RandomWalk): def _compute_kernel_list_series(self, g1, g_list): - self._check_edge_weight(g_list + [g1]) + self._check_edge_weight(g_list + [g1], self._verbose) self._check_graphs(g_list + [g1]) if self._verbose >= 2: import warnings @@ -124,24 +124,24 @@ class SylvesterEquation(RandomWalk): # compute kernel list. kernel_list = [None] * len(g_list) - if self._q == None: + if self._q is None: # don't normalize adjacency matrices if q is a uniform vector. Note # A_wave_list actually contains the transposes of the adjacency matrices. A_wave_1 = nx.adjacency_matrix(g1, self._edge_weight).todense().transpose() if self._verbose >= 2: - iterator = tqdm(range(len(g_list)), desc='compute adjacency matrices', file=sys.stdout) + iterator = tqdm(g_list, desc='compute adjacency matrices', file=sys.stdout) else: - iterator = range(len(g_list)) + iterator = g_list A_wave_list = [nx.adjacency_matrix(G, self._edge_weight).todense().transpose() for G in iterator] - if self._p == None: # p is uniform distribution as default. + if self._p is None: # p is uniform distribution as default. if self._verbose >= 2: - iterator = tqdm(range(len(g_list)), desc='calculating kernels', file=sys.stdout) + iterator = tqdm(range(len(g_list)), desc='Computing kernels', file=sys.stdout) else: iterator = range(len(g_list)) for i in iterator: - kernel = self.__kernel_do(A_wave_1, A_wave_list[i], lmda) + kernel = self._kernel_do(A_wave_1, A_wave_list[i], lmda) kernel_list[i] = kernel else: # @todo @@ -153,7 +153,7 @@ class SylvesterEquation(RandomWalk): def _compute_kernel_list_imap_unordered(self, g1, g_list): - self._check_edge_weight(g_list + [g1]) + self._check_edge_weight(g_list + [g1], self._verbose) self._check_graphs(g_list + [g1]) if self._verbose >= 2: import warnings @@ -162,17 +162,17 @@ class SylvesterEquation(RandomWalk): # compute kernel list. kernel_list = [None] * len(g_list) - if self._q == None: + if self._q is None: # don't normalize adjacency matrices if q is a uniform vector. Note # A_wave_list actually contains the transposes of the adjacency matrices. A_wave_1 = nx.adjacency_matrix(g1, self._edge_weight).todense().transpose() if self._verbose >= 2: - iterator = tqdm(range(len(g_list)), desc='compute adjacency matrices', file=sys.stdout) + iterator = tqdm(g_list, desc='compute adjacency matrices', file=sys.stdout) else: - iterator = range(len(g_list)) + iterator = g_list A_wave_list = [nx.adjacency_matrix(G, self._edge_weight).todense().transpose() for G in iterator] # @todo: parallel? - if self._p == None: # p is uniform distribution as default. + if self._p is None: # p is uniform distribution as default. def init_worker(A_wave_1_toshare, A_wave_list_toshare): global G_A_wave_1, G_A_wave_list G_A_wave_1 = A_wave_1_toshare @@ -186,7 +186,7 @@ class SylvesterEquation(RandomWalk): len_itr = len(g_list) parallel_me(do_fun, func_assign, kernel_list, itr, len_itr=len_itr, init_worker=init_worker, glbv=(A_wave_1, A_wave_list), method='imap_unordered', - n_jobs=self._n_jobs, itr_desc='calculating kernels', verbose=self._verbose) + n_jobs=self._n_jobs, itr_desc='Computing kernels', verbose=self._verbose) else: # @todo pass @@ -201,7 +201,7 @@ class SylvesterEquation(RandomWalk): def _compute_single_kernel_series(self, g1, g2): - self._check_edge_weight([g1] + [g2]) + self._check_edge_weight([g1] + [g2], self._verbose) self._check_graphs([g1] + [g2]) if self._verbose >= 2: import warnings @@ -209,13 +209,13 @@ class SylvesterEquation(RandomWalk): lmda = self._weight - if self._q == None: + if self._q is None: # don't normalize adjacency matrices if q is a uniform vector. Note # A_wave_list actually contains the transposes of the adjacency matrices. A_wave_1 = nx.adjacency_matrix(g1, self._edge_weight).todense().transpose() A_wave_2 = nx.adjacency_matrix(g2, self._edge_weight).todense().transpose() - if self._p == None: # p is uniform distribution as default. - kernel = self.__kernel_do(A_wave_1, A_wave_2, lmda) + if self._p is None: # p is uniform distribution as default. + kernel = self._kernel_do(A_wave_1, A_wave_2, lmda) else: # @todo pass else: # @todo @@ -224,7 +224,7 @@ class SylvesterEquation(RandomWalk): return kernel - def __kernel_do(self, A_wave1, A_wave2, lmda): + def _kernel_do(self, A_wave1, A_wave2, lmda): S = lmda * A_wave2 T_t = A_wave1 @@ -242,4 +242,4 @@ class SylvesterEquation(RandomWalk): def _wrapper_kernel_do(self, itr): i = itr[0] j = itr[1] - return i, j, self.__kernel_do(G_A_wave_list[i], G_A_wave_list[j], self._weight) \ No newline at end of file + return i, j, self._kernel_do(G_A_wave_list[i], G_A_wave_list[j], self._weight) \ No newline at end of file